Is an investor stolen their profits by mimic investors? Investigated by an agent-based model

Abstract: Some investors say increasing investors with the same strategy decreasing their profits per an investor. On the other hand, some investors using technical analysis used to use same strategy and parameters with other investors, and say that it is better. Those argues are conflicted each other because one argues using with same strategy decreases profits but another argues it increase profits. However, those arguments have not been investigated yet. In this study, the agent-based artificial financial market model(ABAFMM) was built by adding "additional agents"(AAs) that includes additional fundamental agents (AFAs) and additional technical agents (ATAs) to the prior model. The AFAs(ATAs) trade obeying simple fundamental(technical) strategy having only the one parameter. We investigated earnings of AAs when AAs increased. We found that in the case with increasing AFAs, market prices are made stable that leads to decrease their profits. In the case with increasing ATAs, market prices are made unstable that leads to gain their profits more.

• The paper demonstrates that fundamental mimicry stabilizes markets through negative feedback, reducing individual profits as prices converge.
• It employs large-scale agent-based simulations with 1000 normal agents and up to 99 additional agents to examine strategy interactions.
• Technical mimicry amplifies price volatility via positive feedback, enabling increased profits despite the proliferation of similar strategies.

Impact of Mimic Investors on Profitability: An Agent-Based Financial Market Analysis

Model Framework and Methodological Innovations

The paper investigates the countervailing arguments regarding whether investors employing identical strategies—so-called "mimic investors"—diminish or augment individual profitability in financial markets. To empirically evaluate these claims, the authors extend the agent-based artificial financial market model (ABAFMM) by introducing a class of “additional agents” (AAs). These AAs are further partitioned as additional fundamental agents (AFAs), executing fundamental-value strategies, and additional technical agents (ATAs), following technical trading rules based solely on historical price data.

The exchange mechanism in the model uses a continuous double auction, where normal agents (NAs) represent heterogeneous market participants with hybrid trading strategies (combining fundamental, technical, and noise components). The NAs operate with random parameterizations, short-selling is unconstrained, and the system accommodates infinite positions. AAs, on the other hand, are homogeneous populations—either all AFAs or all ATAs—with unified strategic parameters. AFAs act when the price diverges from a fixed fundamental value, while ATAs act on price momentum signals.

Simulation Results and Numerical Findings

Simulations—parameterized with 1000 NAs and up to 99 AAs, running to $t=20\,000\,000$—produce quantitatively robust outcomes:

• Market stability is directly correlated with AFA population size: As AFAs increase, price volatility declines, and the system converges tightly to the fundamental value. This stabilization is achieved via negative feedback, where AFAs absorb deviations, trading against mispricing, but their profits per capita decline as the price gap closes.
• ATA proliferation amplifies market instability and profit: The addition of ATAs increases price volatility via positive feedback, with ATAs reinforcing price trends, thus expanding volatility. Notably, increasing the number of ATAs monotonically raises individual and aggregate profits, contrary to the orthodox expectation in competitive environments. Their trading volume stabilizes after $n_a > 20$, but profit growth persists.

This dichotomy is visually demonstrated by time series plots of the market price. With AFAs, the market price oscillates minimally around the fundamental, whereas ATAs induce large, persistent deviations. The simulated profits indicate that for ATAs, more mimic investors paradoxically benefit individually through self-reinforcing dynamics, whereas for AFAs, mimicry erodes profitability.

Theoretical and Practical Implications

The study delivers a clear contrast between fundamental and technical mimicry in agent-based markets. The negative feedback loop in fundamental strategies leads to market stability but attenuates profit extraction, underscoring the market-making function of value-based strategies. Conversely, technical mimicry exploits and amplifies endogenous price trends, resulting in heightened volatility and increased profit opportunities, a mechanism often associated with liquidity provision, speculative bubbles, and propagation of price swings.

These findings challenge conventionally held beliefs regarding strategic crowding and competitive profit erosion in financial markets, particularly for technical strategies. The results imply that technical mimicry may not undermine profitability, even as the strategy becomes widespread, due to market destabilization effects—creating a risk of systemic instability.

From a regulatory and market design perspective, the outcomes emphasize the necessity of monitoring technical trader concentrations and their amplification mechanisms, as well as the stabilizing contributions of fundamental agents. That agent-based modeling delineates these micro-macro feedback effects not captured in equilibrium models is significant for policy, risk management, and market engineering.

Speculation on Future Developments

The research opens avenues for further exploration of parametric heterogeneity, cross-strategy interactions, and adaptive agent learning in ABAFMMs. Future work may study threshold effects—how increasing technical mimicry might precipitate regime shifts, flash crashes, or persistent non-equilibrium states. Moreover, embedding non-linear or stochastic fundamental valuations or dynamic strategy adoption could enhance realism and predictive utility.

Integration with real market microstructure data and conducting out-of-sample validation could establish empirical relevance. The implications extend to high-frequency trading, algorithmic strategy proliferation, and systemic risk assessment, particularly under conditions of increasing automation and homogeneity in market-participant behaviors.

Conclusion

The paper rigorously demonstrates that the impact of mimic investors depends fundamentally on the underlying trading paradigm. Fundamental mimicry stabilizes prices but attenuates profits through negative feedback, while technical mimicry destabilizes markets, reinforcing trends and increasing profits via positive feedback. These findings, elucidated through large-scale agent-based simulations, offer substantive insight into strategic crowding, endogenous market dynamics, and competitive profitability, carrying implications for both theory and the architecture of financial markets (2603.03671).